Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks

Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks

Citation Author(s):
Quoc Dung
Cao
University of Washington
Youngjun
Choe
University of Washington
Submitted by:
Quoc Dung Cao
Last updated:
Thu, 12/13/2018 - 03:04
DOI:
10.21227/sdad-1e56
Data Format:
License:
Creative Commons Attribution
Dataset Views:
142
Share / Embed Cite

After a hurricane, damage assessment is critical to emergency managers and first responders so that resources can be planned and allocated appropriately. One way to gauge the damage extent is to detect and quantify the number of damaged buildings, which is traditionally done through driving around the affected area. This process can be labor intensive and time-consuming. In this paper, utilizing the availability and readiness of satellite imagery, we propose to improve the efficiency and accuracy of damage detection via image classification algorithms. From the building coordinates, we extract their aerial-view windows of appropriate size and classify whether a building is damaged or not. We demonstrate the result of our method in the case study of 2017 Hurricane Harvey.

Instructions: 

To extract the dataset, please unzip the main file 'Post-hurricane.zip'. There will be 4 folders inside:

  1. train_another : the training data; 5000 images of each class
  2. validation_another: the validation data; 1000 images of each class
  3. test_another : the unbalanced test data; 8000/1000 images of damaged/undamaged classes
  4. test : the balanced test data; 1000 images of each class

All images are in JPEG format, the class label is the name of the super folder containing the images

Dataset Files

You must login with an IEEE Account to access these files. IEEE Accounts are FREE.

Sign Up now or login.

Embed this dataset on another website

Copy and paste the HTML code below to embed your dataset:

Share via email or social media

Click the buttons below:

facebooktwittermailshare
[1] , "Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/sdad-1e56. Accessed: May. 20, 2019.
@data{sdad-1e56-18,
doi = {10.21227/sdad-1e56},
url = {http://dx.doi.org/10.21227/sdad-1e56},
author = { },
publisher = {IEEE Dataport},
title = {Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks},
year = {2018} }
TY - DATA
T1 - Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/sdad-1e56
ER -
. (2018). Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks. IEEE Dataport. http://dx.doi.org/10.21227/sdad-1e56
, 2018. Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks. Available at: http://dx.doi.org/10.21227/sdad-1e56.
. (2018). "Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks." Web.
1. . Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/sdad-1e56
. "Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks." doi: 10.21227/sdad-1e56